“I did not mean to make away with the child, I did not know what I was about”: Autobiographical Traces of Infanticide in Eighteenth-Century Trial Records
Bibliographic record
Abstract
In this essay, I am interested in the possibilities of maternal autobiography in court documents. I focus specifically on the trial records of mothers charged with infanticide between 1700 and 1800. Drawing on the Proceedings of the Old Bailey, 1674-1913, I consider these narratives both through the lenses of legal and social histories of infanticide, and in relation to Marlene Kadar et al.’s notion of “autobiographical traces,” fragmentary stories that emerge when pieces of individual lives are stitched together with the historical, social and political context in which they emerged. The fragments I explore in this essay include not only the limited textual interventions of the accused mothers themselves, as they took the stand to speak in their defense, but also their silences and erasures. In addition to this, I consider the autobiographical potential of these women’s actions and behaviours, as witnessed and deposed by those called to the stand. Finally, I consider the stories of self that emerge from the reproductive and maternal body; that is, I am interested in the ways that bodily stories and understandings inevitably complicate textual and behavioural narratives. This article was submitted to the European Journal of Life Writing on 15 September 2014 and published on 25 June 2015.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".